Abstract
The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ°) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson’s correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m3/m3 for the ascending data, and are 0.012, 0.026, and 0.055 m3/m3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.
Highlights
Soil moisture is widely recognized as an important state variable in land surface processes for regulating the water cycle and energy balance [1]
The calibration results show that the simulated backscatter is in good agreement with the Sentinel-1A
The retrieved soil moisture is in good agreement with using the look-up table (LUT) method
Summary
Soil moisture is widely recognized as an important state variable in land surface processes for regulating the water cycle and energy balance [1]. On the Tibetan Plateau, soil moisture is one of the most sensitive factors influencing the precipitation pattern and water cycle, which will provide feedback to the surrounding climate change [3]. Due to the spatiotemporal variability of soil moisture, accurate and timely measurements at a regional and global scale remain difficult. Remote sensing techniques provide an operational alternative for soil moisture monitoring at different scales [4]. Both active and passive microwave remote sensing techniques have attracted much attention for the mapping of soil moisture due to their high sensitivity to soil permittivity and flexible all-weather and all-time sensing abilities [1,4,5,6,7]
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